Abstract
Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. A quantitative comparison with carefully tuned open-loop sequences and PID controllers is included. The deep reinforcement learning controller achieves the highest performance and requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control. control.
Highlights
The demands on the control system of liquid rocket engines have significantly increased in recent years [1], in particular for reusable engines
The aging of reusable engines requires a robust control system as the performance of engine components might degrade over time, e.g., due to soot depositions [2]–[4], increased leakage mass flows caused by seal aging [5], or turbine blade erosions [6]
A further negative effect is that the temperature in the combustion chamber can rise significantly due to a shift in the mixing ratio, which could reduce the engine’s service life
Summary
The demands on the control system of liquid rocket engines have significantly increased in recent years [1], in particular for reusable engines. Most liquid rocket engines use predefined valve sequences to drive the system from the start signal to a desired steady-state and to shut down the engine safely. These control sequences are usually determined during costly ground tests. With the electrification of actuators and the grown demands, interest in closed-loop solutions has increased recently and will continue to rise in the future when launch vehicles and the associated rocket engines will be designed with multidisciplinary design optimization tools [12]
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More From: IEEE Transactions on Aerospace and Electronic Systems
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